To investigate the self-awareness mechanism for DRL, we paid heed to the self-attention module that was first introduced by [1]} to resolve the inability to translate complex sentences in natural language processing tasks, which enables the memorisation of long source sentences by introducing a dynamic context vector of importance weights. Afterwards, [2]} developed the transformer model, which is a multi-head self-attention module and achieved outstanding performance for translation tasks. Furthermore, [3]} used a variant form of the transformer model to accommodate the varying size of input data from the surrounding vehicles in autonomous driving, particularly in advance of addressing the limitation of the list of feature representations in a function approximation setting and enhancing the interpretability of the interaction patterns between traffic participants. However, the current explorations did not discover an attention mechanism to evaluate the safety concerns of an autonomous vehicle in the driving environment, specifically challenging road traffic junction scenarios.
